In ultrasonic diagnostic system, color flow imaging technology is used to measure the moving status and parameters of blood flow or the tissues within a human body, which includes the direction and speed of blood flow, strength of blood flow and the turbulence associated with the blood flow. And the blood flow status will be displayed with pseudo-color on the screen to help doctor make a correct diagnosis.
The basic principle of color flow imaging technology is based on ultrasound Doppler effect to detect the status of the moving subject. When blood flow within a living subject is subjected to ultrasonic waves sending from the probe, the ultrasonic waves are reflected, refracted, absorbed and backscattered. Because the tissue or blood flow is moving, the frequency of the backscattered waves changes from that of the transmitted waves due to the Doppler effect. For example, the frequency of the received waves from the blood flow moving toward the probe (normally indicated in red in the color flow images) increases, while the frequency of the received waves from the blood flow moving backward (normally indicated in blue) decreases. Therefore, the speed and direction of blood flow can be determined by measuring the phase difference between the received waves and transmitted waves, and then the blood flow status can be displayed on the screen. However, in practical applications, the direction of blood flow velocity, which is calculated by measuring the received waves, always shows abnormities due to various kinds of reasons. For example, blue pixels appear in the blood vessel images while the flow is indicated in red, or red pixels appear in the blood vessel images while the flow is indicated in blue (except the turbulence phenomenon due to maximum Doppler detection limit). Thus, these abnormities will influence the clinical diagnosis.
System noise is regarded as the most intuitionistic one in all kinds of reasons resulting abnormities in the calculation of blood flow direction. Since ultrasonic backscattered wave from blood flow is 40˜50 db lower than that from tissues, as the probing area goes deeper, the amplifier gain has to increase to compensate the depth attenuation, the backscattered wave of blood flow from far field will be immersed in the noise induced by the increasing amplifier gain, thereby causing the calculated direction of blood flow velocity to be abnormal.
Speckle effect is also one of the reasons resulting abnormities in the calculation of blood flow velocity. Speckle effect comes from that quite a lot of scatters may be included in one resolution cell and the phases of the reflected waves from these scatters are different when the backscattered waves arriving at the ultrasonic probe. Therefore, the amplitude of the backscattered waves in some resolution cells will be enhanced after the summation of the backscattered waves from all of the scatters. Whereas, the amplitude of the backscattered waves in some other resolution cells will be weakened after the summation. Under the same noise level, the signal to noise ratio of the weakened resolution cells becomes lower, so that it is easier to cause the calculated direction of blood flow velocity to be abnormal.
Another reason for resulting estimation error for the direction of blood flow velocity comes from the wall filter processing. In most commercial ultrasonic color imaging systems, a fixed cut-off frequency for the high pass filter can be usually selected as the low limit for detectable blood flow velocity, so as to suppress tissue reflected wave. However, the slowly moving velocity of tissue in a 2-D cross section is different from resolution cell to cell. When the cut-off frequency for the high pass filter cannot fit all resolution cells, the wall filtered signals from some resolution cells contain clutter residue. These clutter residual signals generally result estimation error for direction of blood flow velocity. In the vessel with low blood flow velocity, this phenomenon is more obvious.
The reasons resulting estimation error for the direction of blood flow velocity are not limited to above ones. Reverberation can also produce this kind of error. In order to eliminate the abnormal velocities in a color flow image as much as possible, several solutions are given in the art. Those solutions can be divided into two categories, i.e. spatial smoothing and temporal smoothing.
Therein, the methods for spatial smoothing include the one-dimensional smoothing along scan line presented in U.S. Pat. No. 5,042,491 and U.S. Pat. No. 5,653,234, and two-dimensional smoothing presented later in U.S. Pat. No. 5,515,852 and U.S. Pat. No. 5,860,928.
U.S. Pat. No. 5,042,491 discloses a solution for judging the direction of blood flow based on the strength and velocity of blood flow calculated by using auto-correlation algorithm, wherein this solution is directed to cancel the abnormities of blood flow velocities caused by the system noise. When one of or both of strength and velocity of blood flow is greater than a threshold in some resolution cells, the velocity estimation is regarded reliable and the output represents the sign of blood flow velocity. Otherwise the velocity estimation is regarded unreliable, and then the majority direction among the velocity directions of adjacent resolution cells along the scan line is used as the velocity direction of current resolution cells.
Additionally, U.S. Pat. No. 5,653,234 also discloses a solution for spatial smoothing to increase the autocorrelation coefficients or the signal-to-noise ratio of temporal samples, so as to enhance the reliability of velocity estimation. This solution firstly judges the strength of correlator's signals or temporal samples, signal changing speed and signal sum of absolute difference (SAD). When any one of these items meets certain condition, e.g., when the strength of temporal samples is greater than a threshold, the spatial smoothing aperture may be reduced, or even not be done. Otherwise the spatial smoothing aperture is enlarged.
U.S. Pat. No. 5,515,852 firstly discloses a solution utilizing weighted 2-D spatial smoothing to increase signal-to-noise ratio for estimating abnormal blood flow velocity. And it also provides different weighted solutions according to the distribution of strength and velocity estimated by using auto-correlation with regard to different parts of human body. Later, the solution disclosed in U.S. Pat. No. 5,860,928 also utilizes 2-D spatial smoothing to increase signal-to-noise ratio for detection. It differs from U.S. Pat. No. 5,515,852 in that the spatial smoothing is done only when the strength and speed of blood flow are greater than a certain threshold.
Temporal smoothing method firstly appears in the solution disclosed in U.S. Pat. No. 5,215,094. This solution enhances the signal-to-noise ratio in velocity estimation by using temporal recursive filtering. The methods disclosed in U.S. Pat. No. 5,357,580 and U.S. Pat. No. 5,467,770 is a kind of adaptive temporal recursive filtering. The former selects recursion factors according to the speed, while the later selects recursion factors according to the speed and the frame rate. The solution disclosed in U.S. Pat. No. 5,897,502 adds decision and threshold for velocity direction during temporal smoothing so as to enhance the ability for correcting the abnormities of velocity direction resulted by clutter residue and system noise. U.S. Pat. No. 5,860,930 discloses a solution utilizing the strength ratio of adjacent two frames to calculate the factor of temporal smoothing to obtain better image performance of blood flow.
The above two kinds of smoothing methods all attempt to eliminate abnormities in velocity estimation by utilizing the basic discipline of blood flow situation of human body so as to increase the quality of blood flow image. However, in the above existing technologies, spatial or temporal smoothing is performed separately. Therefore, the disadvantages of these prior arts are obvious:
1) Generally, in spatial smoothing methods, simple 2-D spatial smoothing is implemented on only one frame, without considering of the pulsation and periodicity of the blood flow, and thereby it is not sufficient to eliminate abnormities of blood flow velocities without any prior knowledge. Furthermore during 2-D spatial smoothing, unreliable speed value with abnormal direction is also used (for example, system noise may be regarded as reliable velocity even though its phase angle is random). Therefore, the smoothed velocity is unbelievable.
2) Though temporal smoothing considers pulsation and periodicity of blood flow, it requires more image frames of blood flow during a longer period to correct the abnormal blood flow velocities, or it requires to perform more reliable 2-D spatial smoothing for several times. But this kind of processing requires too much memory, then it cannot be realized or the cost will be too high.
Accordingly, since temporal and spatial smoothing is implemented separately in prior art, the conventional methods can not guarantee that pulsation and periodicity of blood flow (especially when frame rate of blood flow imaging is high) is considered during processing, so that these methods fails to eliminate the estimation error of blood flow velocity as desired.
Therefore, a decision method is required to judging the direction of blood flow accurately with high processing speed and low cost, and thereby the estimation error aroused by the factors such as system noise could be eliminated furthest.